## Hardware Breakpoint implementation in BCC eBPF Summit Manali Shukla Aanandita Dhawan Maneesh Soni ## 01 ## Hardware breakpoint Memory watchpoint Used in debuggers Elegant mechanism to monitor memory access Perf hardware breakpoint implementation: mem:[:access] [Hardware breakpoint]

patterns and build robust applications ## The Art of Writing Efficient Programs An advanced programmer's guide to efficient hardware utilization and compiler optimizations using C++ examples Fedor G. Efficiency and performance • Understanding the hardware and using it efficiently – Computing resources of a CPU - Pipelining Branch prediction and hardware loop unrolling • Conditional code vs efficiency CPU HARDWARE ALL THE TIME ## • What determines performance? • Optimal algorithm: – get the result with minimal work • Efficient use of language: – do not do any unnecessary work ## • Efficient use

Performance Engineering Being Friendly to Your Hardware ## I GNAS BAGDONAS ## Being Friendly to Your Hardware Performance Engineering A gentle introduction to hardware for software engineers Where does C++ run jpg)  - Usable utilization is quite far away from theoretical limit • Bandwidth vs latency 

# Chapter 4 - Efficient Architectures “Any sufficiently advanced technology is indistinguishable from magic.” — Arthur C. Clarke, “Hazards of Prophecy: The Failure of Imagination” (1962) “Any technology gain orders of magnitude in terms of footprint or quality, we should consider employing suitable efficient architectures. The progress of deep learning is characterized by the phases of architectural breakthroughs deployment challenges. What good is a model that cannot be deployed in practical applications! Efficient Architectures aim to improve model deployability by proposing novel ways to reduce model footprint

resources. Alternatively, we can base the search approach on the budget allocation to cap the resource utilization. Multi-Armed Bandit based algorithms allocate a finite amount of resources to a set of hyperparameter or exceeded the contemporary state of the art models. These child networks were smaller and more efficient than the human designed models. However, the key contribution of NASNet was the focus on predicting

# Chapter 1 - Introduction to Efficient Deep Learning Welcome to the book! This chapter is a preview of what to expect in the book. We start off by providing an overview of the state of deep learning introduce core areas of efficiency techniques (compression techniques, learning techniques, automation, efficient models & layers, infrastructure). Our hope is that even if you just read this chapter, you dirty with practical projects. With that being said, let's start off on our journey to more efficient deep learning models. ## I ntroduction to Deep Learning Machine learning is being used in countless

## Designing Fast and Efficient List-like Data Structures ## YANNIC BONENBERGER ## List-like data structures • std::vector • std::list • std::deque ## std::vector • C++ version of the array-list data

recall or other performance metrics). We designate a new model training setup to be more sample efficient, if it achieves similar or better performance with fewer data samples when compared to the baseline same accuracy by seeing a smaller number of samples, that process would be sample efficient. Similarly, a sample efficient model training process requires fewer samples to achieve the same performance, which worth of training time by terminating the training early, if we adopt this hypothetical sample efficient model training. Sample Efficiency ![Image](/uploads/documents/3/1/f/8/31f883e49843cb9d0ee0ee8057c5fabe/p2_1

of parameters, but this could hurt the quality. Compression techniques are used to achieve an efficient representation of one or more layers in a neural network with a possible quality trade off. The constrained environment like a mobile device. To summarize, compression techniques help to achieve an efficient representation of a layer or a collection of layers, such that it meets the desired tradeoff goals chosen to work with Tensorflow 2.0 (TF) because it has exhaustive support for building and deploying efficient models on devices ranging from TPUs to edge devices at the time of writing. However, we encourage

## +23 ## Leveraging the Power of C++ for Efficient Machine Learning on Embedded Devices ADRIAN STANCIU # Leveraging the power of C++ for efficient machine learning on embedded devices Adrian Stanciu automation (e.g. thermostats) ▶ Medical equipment (e.g. pacemakers) ## ▶ Characteristics: ▶ Limited hardware resources ▶ Low power consumption ▶ May have real-time performance constraints ## Machine learning usage ▶ Offline operation ▶ Improved privacy ## ▶ Disadvantages: ▶ Compatibility with various hardware and software platforms ▶ Slower updates ## Using C++ for machine learning on embedded devices